Predictive health behavior modeling using multimodal feature correlations via Medical Internet-of-Things devices

Due to the advent of IoT (Internet of Things) based devices that help to monitor different human behavioral aspects. These aspects include sleeping patterns, activity patterns, heart rate variability (HRV) patterns, location-based moving patterns, blood oxygen levels, etc. A correlative study of these patterns can be used to find linkages of behavioral patterns with human health conditions. To perform this task, a wide variety of models is proposed by researchers, but most of them vary in terms of used parameters, which limits their accuracy of analysis. Moreover, most of these models are highly complex and have lower parameter flexibility, thus, cannot be scaled for real-time use cases. To overcome these issues, this paper proposes design of a behavior modeling method that assists in future health predictions via multimodal feature correlations using medical IoT devices via deep transfer learning analysis. The proposed model initially collects large-scale sensor data about the subjects, and correlates them with the existing medical conditions. This correlation is done via extraction of multidomain feature sets that assist in spectral analysis, entropy evaluations, scaling estimation, and window-based analysis. These multidomain feature sets are selected by a Firefly Optimizer (FFO) and are used to train a Recurrent Neural Network (RNN) Model, that assists in prediction of different diseases. These predictions are used to train a recommendation engine that uses Apriori and Fuzzy C Means (FCM) for suggesting corrective behavioral measures for a healthier lifestyle under real-time conditions. Due to these operations, the proposed model is able to improve behavior prediction accuracy by 16.4%, precision of prediction by 8.3%, AUC (area under the curve) of prediction by 9.5%, and accuracy of corrective behavior recommendation by 3.9% when compared with existing methods under similar evaluation conditions.


Introduction
As a consequence of developments in computer technology and electrical engineering, everything has become more compact and is now capable of connecting to the internet.This covers other electronic gadgets as well, such as sensors and smartwatches, in addition to mobile phones.As a direct consequence of this, the number of distinct device types is constantly growing.It is projected that there will be 30 billion connected devices in use by the year 2025 [41,20,15].As a result of this, brand-new applications for connected devices will significantly impact the way we live our lives.The Internet of Things (IoT) is now a concept that is plausible as a result of the development of new technologies and devices.The Internet of Things (IoT) refers to a network of connected physical devices that are able to speak with one another and share data with one another.These things are enabled to connect with one another, interact with one another, and share data thanks to the incorporation of electronics, software, actuators, and connectivity.The ecosystem that supports the Internet of Things (IoT) is really rather complicated since it integrates a variety of technologies as well as fields of study.
Recent developments in the Internet of Things have had an effect on a wide range of applications, including, amongst others, smart cities, smart homes, smart health care, and smart industries [20].Work in [13,26,18] proposes use of Graph Convolutional Network (GCN) like methods, in spite of the fast expansion of the Internet of Things (IoT), there are a number of challenges that hinder these systems from being as effective as they may be.To begin, it is difficult for different devices to interact with one another since there are so many different communication protocols and device standards.A standardized protocol is necessary in order for devices connected to the Internet of Things to successfully interact with one another.Because of this, there is still a huge problem with interoperability in the internet of things.
The vast majority of applications for the Internet of Things that have been the subject of textual analysis have varying designs and place constraints on the manner in which data may be used via digital twins [39,28,36].Second, recent developments in the field of computer science have made it possible to mass produce a wide range of low-cost sensors that can be set up in a number of different ways to meet a variety of needs.Because of their cheap cost, solid security features, and low power consumption, more and more people are adopting them for a wide range of occupations.This applies to both experienced workers and beginners.They are producing a tremendous amount of data as a direct result of this, which is quite significant.On the other hand, platforms and apps that offer clear and standard data collecting mechanisms are still in their infancy [4,16,38].In conclusion, the processing and analysis of data from a wide variety of sensors and devices connected to the Internet of Things allows an expanding number of systems to make decisions based on the collected data [4].In order to do this, an integrated solution that are based on machine learning for the purpose of data collecting, analysis, and the development of intelligent settings is required.
For instance, the authors of [25,44,40] installed sensors in a variety of positions all over a house, and then examined the data that they acquired from those sensors.A support vector machine (SVM) was applied after a part of the data was manually labeled in order to identify the different activities with an accuracy of 84%.[46,22,33] makes forecasts about the times at which a number of forthcoming events will take place by analyzing the data collected by sensors that are installed on in-house testbeds.The authors started with the presumption that an algorithm for recognizing activities already existed, and that the outputs of this algorithm could be used to train the algorithms that they provided.However, they did not give any evidence to support this assumption.
MavHome (Managing an Adaptive Versatile Home) [27,10] is one further illustration of how machine learning methods may be used to assist persons in feeling more at ease while also saving money on operational expenditures.The fundamental purpose of MavHome is to provide customers with a higher degree of control over their movement.MavHome is working to predict how people will get about in the future by using a variety of technologies, including machine learning, robotics, database management, mobile computing, and more.The coverage area has been segmented into zones or sectors so that any prospective migrations that take place between the zones may be visually shown.After that, a graph is constructed that is representative of the data.According to the findings of this study, one of the most important skills of intelligent settings should be the ability to pinpoint the location of an object [43,8,35].
CASAS [21,42,34], sometimes referred to as a "smart home in a box," is software that automates processes via the use of methodologies derived from the field of machine learning.Fig. 1 depicts a setup in which a number of sensors are installed in different areas throughout a home, and the information that these sensors collect is then subjected to further analysis.With an accuracy of 84%, it is able to determine the sort of activity that a person participates in based on the data that has been tagged.When developing CASCAS, our group kept in mind how important it was to minimize wasteful use of energy.In the not-too-distant future, CASAS could be able to automate the planning of the residential sector if it receives more data and makes its algorithms more sophisticated.In order to obtain data that may be used to evaluate the behavior of individuals, the authors of [9,11] developed a system that is made up of 20 binary sensors that connect with each other using ZigBee.This system is designed to collect information.For real-time use cases, data is collected over the course of a two-month period in two different households that are occupied by a variety of people.
In this research, a behavior modeling approach is proposed that uses deep transfer learning analysis and multimodal feature correlations on medical IoT devices to help predict future health (hereinafter, "BMF2CTL").The suggested approach first gathers extensive sensor data about the patients and compares it to their current medical problems.The extraction of multidomain feature sets, which help in spectrum analysis, entropy assessments, scaling estimates, and window-based analysis, is how this correlation is accomplished.These multidomain feature sets are chosen using a Firefly Optimizer (FFO), and then they are used in the training of a Recurrent Neural Network (RNN) Model, which helps with the prediction of various illnesses.These predictions are utilized to train a recommendation engine that makes use of Apriori and Fuzzy C Means (FCM) to advise corrective behavioral adjustments for a better living under real-time settings.This is done with the intention of encouraging individuals to lead healthier lifestyles.Further to discuss in detail about the related studies and problem with existing approaches, this paper continues with literature review in the next section.

Reliability and adaptability of deep learning in medical applications
Deep learning models, like RNNs, can lack interpretability.It is important to discuss how the model's predictions can be reliable, useful and accurate to the healthcare professionals to ensure trust and adoption in real-world applications.In the proposed solution, we have considered this issue seriously and have implemented strategies to enhance model interpretability and facilitate trust and adoption.Here's an in-depth discussion of how we address this challenge: Explainable AI (XAI) Techniques: We employ XAI techniques to identify and highlight the most influential features contributing to the model's predictions.By presenting feature importance scores, we enable users and healthcare professionals to understand which aspects of the input data played a significant role in a particular prediction.
Attention Mechanisms: In the context of RNNs, attention mechanisms can be employed to visualize the parts of the input sequence that the model focuses on during prediction.These visualizations can help users see which temporal patterns or behavioral cues led to a specific outcome.

LIME and SHAP Values:
We use techniques like Local Interpretable Model-agnostic Explanations (LIME) and Shapley additive explanations (SHAP) to provide post-hoc interpretability.These methods generate simplified, locally faithful models around specific predictions, making it easier to explain why the model made a particular decision.
Interpretability Module: We integrate an interpretability module into our system, which generates textual or graphical explanations for model predictions.These explanations are designed to be user-friendly and accessible to healthcare professionals and individuals without deep technical knowledge.
Interactive User Interface: We develop a user interface that allows users to interact with the model and explore predictions intuitively.Users can input queries, view explanations, and access visualizations, fostering trust and transparency in the model's decision-making process.
Ethical and Regulatory Compliance: We adhere to relevant regulatory requirements for healthcare AI, including providing transparency in model explanations.Compliance with regulations like HIPAA ensures that ethical and legal aspects are maintained.
Real-world Use Cases and Feedback Loop: We conduct extensive clinical validation studies involving healthcare professionals to evaluate the model's predictions in real-world scenarios.Feedback from these studies is incorporated to refine the model's interpretability features.It has been done through our medical center in coordination with Manipal Hospitals, India.
Transparency and Documentation: We maintain clear and comprehensive documentation that outlines the model's architecture, training data, and validation processes.Transparency and accountability in research and development are essential to building trust.
In conclusion, we recognize the importance of model interpretability for real-world adoption in healthcare applications.By combining XAI techniques, post-hoc interpretability, user-friendly interfaces, education, regulatory compliance, clinical validation, and transparency, we aim to ensure that our deep learning model's predictions can be effectively explained to both users and healthcare professionals.Our approach prioritizes trust, transparency, and user-friendliness, facilitating the seamless integration of AI-driven insights into clinical practice while addressing the challenges associated with model interpretability.

Practical contributions and novelty
The novelty and contributions of this work are substantial, with profound implications for the field of healthcare and behavioral analysis: Multimodal Feature Extraction: The proposed model introduces a novel approach to behavioral analysis by employing multimodal feature extraction techniques.By integrating spectral analysis, entropy evaluations, scaling estimation, and window-based analysis, the model captures a comprehensive set of features from diverse data sources, including sleep data, heart rate variability, motion data, and more.This multi-domain feature extraction significantly differs from traditional unimodal approaches, enabling a deeper understanding of behavioral patterns.
Firefly Optimization for Feature Selection: Using the Firefly Optimizer (FFO) for feature selection is a unique and innovative contribution.FFO maximizes inter-class variance, leading to the identification of highly discriminative features.This optimization process improves the accuracy of behavioral analysis and reduces feature redundancies, streamlining the input data.This novel feature selection method has wide-ranging applications beyond healthcare, such as data preprocessing for various domains.
Integration of RNN with LSTM: While Recurrent Neural Networks (RNNs) are commonly used for sequence data, integrating Long Short-Term Memory (LSTM) units within the RNN is a notable innovation.LSTM's ability to capture long-range dependencies in behavioral data enhances prediction accuracy.This contribution is particularly impactful in healthcare, where the ability to foresee health conditions with higher Precision can lead to timely interventions and improved patient outcomes.
Real-Time Recommendation Engine: The recommendation engine, incorporating Apriori and Fuzzy C Means (FCM), is designed for real-time use cases.This feature sets the proposed model apart from existing systems, as it predicts health conditions and offers immediate corrective behavioral measures.This real-time guidance has substantial implications for individuals seeking to make positive lifestyle changes to mitigate health risks.
Benchmarking and Validation: The rigorous benchmarking against state-of-the-art models, coupled with comprehensive validation against diverse datasets, demonstrates the robustness and generalizability of the proposed approach.This contributes to the trustworthiness and widespread adoption of the model in healthcare settings.
The impact of these contributions is far-reaching.In healthcare, the ability to accurately predict health conditions and provide realtime recommendations for lifestyle modifications can significantly reduce healthcare costs, improve patient outcomes, and enhance overall well-being.Beyond healthcare, the multi-domain feature extraction and optimization techniques introduced in this work have the potential to benefit various domains that deal with complex, multimodal data.The model's ethical considerations also set a responsible precedent for AI-driven healthcare applications, ensuring that data privacy and consent are prioritized.This work's novelty lies in its holistic approach to behavioral analysis, optimization, and real-time recommendation, with implications for healthcare, data science, and ethical AI practices.It substantially impacts improving the quality of healthcare delivery and the responsible use of AI technologies.

Literature review
Various theoretical frameworks may be used to examine human behavior.Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), data analytics (DA), and cloud computing are influencing the job sector (according to [24,5].Businesses undergoing a digital transition will need to extend their skill pool as new technologies emerge.To fulfill future needs, businesses depend on their present staff and invest in the education and training of new personnel.The effectiveness of an ambidextrous strategy is strongly dependent on the ambidextrous learning intentions and behaviors of employees.The S-Curve method is used to examine employees' propensity and resistance to learn new skills.Identifying and evaluating the workplace factors that lead to ambidexterity.Focus groups, questionnaires, interviews, and enterprise resource planning (ERP) data were used to identify key features inside a worldwide firm.There is a nonlinear S-curve link between the age of employees and their unwillingness to undertake retraining.It also discusses why and how the portions of the S-impacting Curve restrict drilling and mining.
A sustainable sharing economy may be feasible if incentives in the form of cryptoeconomic tokens based on block-chain technology are used.However, designing effective cryptoeconomic incentives is challenging because to the size of data on how digital currencies influence the sharing behavior of individuals.Using the self-determination theory as a lens, this study explores the effect tokens have on individuals' behavior during information exchange.Using a randomized controlled experiment with a 22 factorial design and 132 participants, the influence of two token incentives on human information-sharing behavior is studied.Tokens are used to facilitate the transmission of information between parties.By accumulating tokens, it is possible to accumulate wealth and renown.Participants in the study also examined how effectively information was contextualized.Previous research has critiqued the emphasis placed on production above quality.This research not only verifies existing effects such as the substitution of extrinsic tion with incentives, but also indicates the cumulative benefit of employing many tokens in concert.The findings are reviewed in light of contemporary workplace professionalism and ethical norms.Theoretical and empirical research assists the community in developing individual incentives for the sharing economy that are successful.
The [2] discusses the use of machine learning and deep learning algorithms to help physicians diagnose patients remotely.It also highlights the importance of protecting healthcare data using federated learning technologies.In [3] proposes a method to detect sadness in social media by analyzing personal remarks.The method uses attention networks with covert levels of self-attention and an extended emotion lexicon to achieve high accuracy in identifying depression symptoms from internet forums.In [14] presents a new approach for disease detection using artificial intelligence and deep learning techniques.The proposed methodology achieves a disease detection rate of 92%, which is significantly better than higher-level disease detection models.
Discuss [45,1,29,31,12] Regarding the Cyber Physical Social Energy System, it is evident that the human element is the most important.Its expansion is driven by the interaction between the social and energy systems.In the subject of CPSES, the influence of transformational leadership on environmentally aware team behaviors is a strongly contested topic.Leadership has the ability to hasten the development and deployment of the Power Energy Internet Platform Technology.Using the Affective Events Theory as a framework, this paper investigates the impact of transformational leadership on the environmentally aware behaviors of a team.19 energy businesses in the Pearl River Delta conducted a survey of 3,821 employees and managers representing 457 teams.This research indicated that teams with a transformational leader fared better on measures of environmental sustainability.This proximity to nature enhances eco-friendly team behavior and transformational leadership.The relationship between transformational leadership and a feeling of belonging to the natural environment is adversely regulated by work concerns, but it is favorably influenced by team task performance.This essay broadens the applicability of the AET theory, explores the relationship between transformational leadership and team environmental practices, and provides areas for further research.
According to research, an Internet user's attitude and viewpoint may influence their susceptibility to phishing efforts.Phishing scammers adopt a systematic technique to deceive their prey into divulging personal information.It is essential to understand the attitudes and behaviors that may enable phishing attacks.By doing so, a deeper understanding of the origins of phishing, create prevention measures, and restrict its consequences at every stage will be achieved.This study examines the influence of risk and decision-making styles during the three stages of phishing victimization.The participants participated in a risk-taking game and answered questions on two psychological measures to assist researchers comprehend their behavior.Throughout all three stages, they were treated to a simulated phishing attempt to determine their susceptibility to such an attack.The different stages of phishability are impacted by both gender and a person's risk-taking tendency, as discovered.Other direct and indirect behavioral aspects may be the subject of future investigation.The methodology and outcomes of this research might be included into a bigger framework designed to eliminate phishing at its source.
Teaching those with motor impairments to drive and providing those without impairments with an alternate method of driving utilizing brain impulses as opposed to limb signals is a promising topic of study.The BCVs' longitudinal driving ability falls short of human standards at high speeds.This research describes a unique predictive control strategy based on driver behavior and vehicle physics, which is expected will improve brain-controlled driving over the long run.The proposed method preserves driver convenience, back-end security, and brain-based control authority.To validate the proposed strategy, this research conduct driverand-hardware-in-the-loop testing for three separate use scenarios.According to the results, the proposed method preserves driver comfort, rear end safety, and the principal driving function.
In mixed-traffic circumstances involving CAVs and human driven vehicles, human drivers may display a variety of car-following behaviors.In this study, the proposed model use Inverse Reinforcement Learning (IRL) to simulate the responses of several human drivers following a CAV and a second human-driven vehicle.The learned driver behavior model accurately and reliably describes individual driving tendencies in a variety of traffic situations.This interaction is used to study the energy efficiency of both the CAV and the human-driven vehicle, since various driving styles have distinct impacts on gas economy.In a car-following scenario, research indicates that a CAV may dramatically cut fuel usage compared to a human-driven vehicle.Due to individual variances in tastes and perceptions, the degree to which persons who have been tested as drivers may benefit from CAV varies.These findings suggest that communications between humans and CAVs may improve the fuel efficiency of mixed-traffic vehicles.
In people-centric AI and video-based IIoT systems, such as the Industrial Internet of Video Things (IIVT), it is becoming more crucial to comprehend human behavior.Deep computing models for IIoVT applications are less beneficial and efficient due to the high cost of computational resources such as high-performance processor units and huge memory.In this research, a deep model for IIoVT applications based on a tensor-train approach for assessing human behavior in movies is presented.A combination of artificial intelligence and prefrontal IIoVT may increase precision and training efficiency.This research supplemented the conventional CNN with the recurrent neural network approach, which takes into consideration the relationship between successive deep features, in order to improve video representation over time and increase accuracy.In order to enhance spatial-temporal inference, this model use self-critical reinforcement learning to the process of parameter learning.The tensor-train approach turns the parameter matrix into a tensor space and utilizes tensor decomposition to minimize the number of training parameters in order to reduce the parameter storage size for deep neural network and edge device deployment.
As a conclusion, a comprehensive testing to evaluate our system is required.The results indicate that our strategy may improve the training efficiency and information retention of the deep learning model.In BCI research, the classification of EEG data for the prediction of human intentions and behaviors is a recurrent difficulty.Recent study has shown that CNN is improving its classification of EEG data.Existing methodologies for identifying EEG data using CNN are not precise enough.When attempting to identify EEG data, the majority of existing algorithms simply use the final layer of feature maps from a convolutional neural network (CNN), leaving out crucial contextual and granular features.After translating raw EEG data into time-frequency images using STFT, a CNNbased multi-scale EEG signal classification system is shown.The time-frequency image is then loaded into a multi-scale convolutional neural network (CNN) model for the classification of EEG signals.The global and regional settings are taken into account by the CNN model with several scales.Using data from BCI contest IV benchmark dataset 2b, the proposed method is evaluated.The classification accuracy is improved by 10.4, 5.5, and 16.2 percentage points, respectively, in comparison to cutting-edge techniques such as artificial neural networks, support vector machines, and stacked auto-encoders.
In [6,47], an ensemble RGB-S deep neural network is utilized to detect behaviors in films by combining RGB images with skeletal data from a home service robot action recognition database.The ensemble model consists of three distinct elements.The first one recognizes activities in movies using a CNN built using a pre-trained ResNet101 model.The 2D (RGB) action sequence images are sent into the CNN, which subsequently processes the input using a long short-term memory (LSTM) network.The second stream employs a 3D convolutional neural network to analyze temporal and spatial data and includes native video control.The 3D CNN is trained using the R3D-18 model (RGB 3D-CNN).The PEI approach converts the skeleton sequence into a black and white image.CNN provides images with alterations (Skeleton PEI-2D-CNN).This method depicts the spatial and temporal characteristics of video activity using skeletal sequences, 3D films, and 2D and 2D sequence images.Using the vast ETRI-Activity3D database for video behavior recognition, the suggested deep neural network is assessed.Compared to earlier models, our technique showed a 93.2% success rate in a study comparing several patients.The study presented in [47] examines the feasibility of using grip time to transmit an emotion from a robot to a group of persons enjoying a movie experience.Many previous efforts on human-robot touch contact have forgotten to take time into consideration while focusing on the robot's touch behaviors as a way of transmitting emotion.This message's grip time was analyzed in order to communicate both pleasant and frightening emotions.Participants assessed the commencement and length of touch (grip) activities using a robot and visual cues.Horror films are preferred to be seen first, while comedies are preferred to be viewed last.To replicate human-like touch behaviors on an Android robot, a probabilistic distribution fitting technique was utilized to define times and durations.In [37], a unique dual encoder-decoder architecture for effectively segmenting anatomical features in chest X-ray images is proposed.The proposed approach can accurately segment both the larger and smaller entities in chest Xrays.However, pneumonia and covid categorization cases must be used to improve the performance of the anatomical structures segmentation framework.
For the research reported in [30], forty-four human participants interacted with a dynamic system forty times over the course of one week.There have previously been specified four categories.Even while both teams use the same dynamic system, their responses to directives differ.The frequency of all reference instructions is defined to fall between 0 and 0.5 Hz.The control strategy of each subject is calculated using a subsystem identification method (feedback and feedforward).Experimental and identifier data are used to evaluate the effect of command-following tasks on subject performance and control approaches.This capacity to match the reference command's phase explains why subjects can more readily follow a chirp than they can a sum of sinusoids.Three properties of the identified controllers may explain differences in the command-following ability of responders across tasks.First, a feedforward highgain feedback controller; second, an accurate inverse dynamics approximation; and last, a feedforward time-delay compensating feedback loop.When requested to modify their technique of control, responders often exceed the request.People continue to use the same feedforward model of inverse dynamics in their thoughts even when the command changes.In conclusion, this research demonstrates that although individuals do benefit from command prediction in terms of enhanced performance (when it is available), they may obtain comparable outcomes without it under specific conditions.
Despite confusing instructions, subjects learn to apply high-gain feedback controllers to improve performance.The use of artificial intelligence to recognize anomalous human behavior in Internet of Things video data has been proposed [17].Using cloud-based deep learning anomaly detection algorithms, data exhibiting suspicious behavior is subjected to supervised learning.This framework for supervised learning disregards variety and unpredictability in open-scenario circumstances, focusing instead on preset categories of anomalous behavior.This research offers a network-periphery service that searches cloud resources for suspicious behavior insights.This approach is able to identify anomalous behavior by combining global and local fine-grained action cycle alignment.In order for abnormal behavior detection models to be able to predict test samples whose categories did not appear during training, this model proposes a cycle clustering-based active label learning algorithm that improves data transmission between the edge and the cloud and makes cloud-based model updates more effective by leveraging Guided Neural Networks (GNN).
An intra-and inter-modal fusion for depression detection with multi-modal information from the Internet of Medical Things (IIFDD) is presented in [7].This paper explores depression diagnosis limitations and introduces a new framework for enhanced detection using IoMT.Demonstrating its superiority, this study opens doors for improved depression diagnosis methods.But neglecting social interactions, lifestyle patterns, or contextual data might limit the comprehensive understanding of depression.Model interpretability is crucial, especially in medical applications, and needs more comprehensive examination and validation.Another Multimodal Deep Learning for Activity Detection from IoT sensors (MDL-AD) is presented in [19].This method improves human activity detection in the IoT, achieving 97% accuracy on the STISEN and GAIT datasets, surpassing prior methods.Even though the MDL-AD approach shows promising results on the STISEN and GAIT datasets, the evaluation lacks diversity and representation of various real-world scenarios.Relying solely on specific datasets for validation might lead to overfitting or bias towards those datasets' characteristics.
Another contribution, "HealthPrism" an interactive visual analytics system, addresses challenges in analyzing multimodal data for comprehensive health insights [23].The correlation between children's characteristics and health status has been extensively studied.But missing data sources like genetic information, environmental exposures, or dietary habits limit the system's comprehensive analysis.Health data, especially concerning children, raises significant ethical concerns regarding privacy, consent, and responsible data usage.IoT integrated with the cloud, coupled with deep learning, enables accurate disease prediction (PMS-DL), as proposed in [32].The LSTM-based-healthcare system achieves 96.99% accuracy, surpassing traditional methods.The proposed model doesn't discuss how the IoT-based system incorporates or addresses individual risk factors, lifestyle data, or behavioral patterns that could aid in disease prevention.
It is clear, in light of the results of this study, that the majority of these models make use of a variety of factors, which contributes to a reduction in the accuracy of the models.Because of the intricacy of these models and the lack of flexibility in the parameter options that they provide, the vast majority of them are not scalable for real-time application use cases.This paper discusses how to construct the proposed behavior modeling technique in next section, that may predict future health via multimodal feature correlations utilizing medical IoT devices and deep transfer learning analysis.In the fourth section, the performance evaluations necessary to determine the performance of the model and compared it to the performance of many alternative techniques when applied to the identical evaluation conditions.The model was tested using Internet of Things sensors that were set up in the environment.This study comes to a close with some general observations on the model that was proposed as well as some ideas for how the model may be improved so that it could be more adaptable to real world scenarios.

Problem statement
The Related Works section highlights the gaps in existing literature related to behavior modeling and health prediction.While prior research has explored various behavior analysis models, the identified gaps revolve around complexity, limited scalability, and a lack of interpretability in these models.Many existing techniques exhibit challenges in providing interpretable insights into predictions, hindering their adoption among healthcare professionals.Scalability limitations have also been observed, restricting their applicability in real-time, large-scale healthcare settings.Furthermore, the absence of personalized recommendations in these systems highlights a crucial gap in addressing individual health needs effectively.The proposed model aims to bridge these gaps by offering a comprehensive, interpretable, and scalable behavior modeling approach that leverages deep learning and multi-domain feature extraction, ultimately contributing to more effective, personalized, and real-time healthcare recommendations.

Motivation
Given the limitations of existing systems, there is a pressing need for advanced behavior modeling approaches that can provide interpretable, scalable, and personalized recommendations.In an era where data-driven healthcare is gaining prominence, leveraging medical IoT devices for behavior analysis can enhance patient outcomes, enable proactive healthcare interventions, and optimize lifestyle choices [13].The proposed system aims to bridge the existing gaps in behavior analysis by leveraging deep learning, multidomain feature extraction, and advanced optimization techniques, ultimately contributing to more effective, personalized, and realtime healthcare recommendations.

Objectives
The problem statement addressed in this research revolves around the need for an advanced behavioral analysis model that can accurately predict health conditions and provide real-time recommendations for lifestyle modifications.The scope of the research encompasses the development of a comprehensive system that leverages medical IoT devices to collect multimodal data, extracts meaningful features using innovative techniques, employs deep learning methodologies such as RNNs and LSTMs for prediction, and integrates a real-time recommendation engine based on Apriori and Fuzzy C Means (FCM).This system is designed to address the limitations of existing behavioral analysis models, which often lack accuracy, scalability, and real-time capabilities.The research seeks to contribute novel solutions to these challenges while adhering to ethical considerations related to data privacy and consent.Ultimately, the scope of this work extends to improving healthcare outcomes, reducing costs, and advancing the responsible application of AI in healthcare and beyond.Existing behavior analysis systems and healthcare solutions exhibit several limitations.These systems cannot often provide interpretable insights into their predictions, hindering their acceptance and trust among healthcare professionals and individuals.Scalability challenges have also been observed, limiting the real-time application of such systems in large-scale healthcare settings.Moreover, many existing solutions offer generalized recommendations without personalization, which may not be optimal for individual patients.Addressing these limitations is crucial to advancing the field of healthcare analytics.

Proposed behavior modeling approach
Based on the brief review of existing behavior analysis models, it can be observed that most of these techniques vary in terms of used parameters, which limits their accuracy of analysis.Moreover, most of these models are highly complex and have lower parameter flexibility, thus, cannot be scaled for real-time use cases.To overcome these issues, this section discusses design of a behavior modeling method that assists in future health predictions via multimodal feature correlations using medical IoT devices via deep transfer learning analysis.Flow of the model is depicted in Fig. 1, where it can be observed that the proposed model initially collects large-scale sensor data about the subjects, and correlates them with the existing medical conditions.This correlation is done via extraction of multidomain feature sets that assist in spectral analysis, entropy evaluations, scaling estimation, and window-based analysis.These multidomain feature sets are selected by a Firefly Optimizer (FFO) and are used to train a Recurrent Neural Network (RNN) Model, that assists in prediction of different diseases.These predictions are used to train a recommendation engine that uses Apriori and Fuzzy C Means (FCM) for suggesting corrective behavioral measures for a healthier lifestyle under real-time conditions.

Firefly Optimizer (FFO):
The FFO was employed to select the most relevant features from the multi-domain feature set.Configuration hyperparameters, such as the density of fireflies, iteration capacity, and inter-firefly learning rate, were set.The FFO iteratively selected features, estimated their brightness and reconfigured fireflies to maximize inter-class variance.This process led to the selection of a subset of feature sets.Recurrent Neural Network (RNN) with LSTM: The selected feature sets were fed into an RNN architecture, specifically utilizing Long-Short-Term Memory (LSTM) cells.The LSTM network enhanced the feature sets by calculating initialization vectors, functional feature vectors, output feature vectors, and convolutional feature vectors for multiple iterations.This resulted in a refined and augmented feature representation.Behavioral Class Prediction: The RNN layers were fused to predict behavioral classes.We used softmax activation to estimate output classes based on weights and biases.The output classes were categorized into Low Effect (LE=1), Medium Effect (ME=2), and High Effect (HE=3) based on their impact on health conditions.Recommendation Engine and Clustering: Our recommendation engine employed Apriori and Fuzzy C Means (FCM) clustering techniques.Correlation values between different behavioral parameters were used to generate a linkage map, allowing us to estimate the dependence of one category on others.Automatic clustering was performed, and clusters with similar features were identified.Features within the same clusters were associated with similar health conditions.
From the overview presented in Fig. 1 it can be observed that the proposed model initially extracts multidomain feature sets for representing data from different sources.These sets are extracted by evaluation of Fourier components via equation (3.1) and all the notations used in this work has been summarized in Table 1.

𝐷𝐹 𝑇 𝑖
where,   represents count of total number of input samples, while x represents value of the samples.These features assist in calculation of frequency components and their spectrum for identification of temporal patterns.Frequency features are extended via extraction of cosine features via equation (3.2), These components assist in analyzing entropy levels for different behavioral data samples.Similarly, the window-based convolutional components are extracted via equation (3.3), A combination of these feature sets is used to generate a Behavioral Feature Vector (BFV), which contains redundancies due to extraction of similar feature sets.These redundancies are reduced via use of a Firefly Optimizer, that assists in maximizing inter-class variance, via the following operations, • To initialize the Firefly optimizer, a set of configuration hyperparameters are setup as follows, 1. Density of fireflies, which represents total number of reconfigurations (  ) 2. Iteration capacity of fireflies, which represents total count of cycles for which the fireflies will be reconfigured (  ) 3. The inter-firefly learning rate, which will assist the fireflies to socially learn from each other (  ) • Initially, a set of   firefly particles are generated as per the following process, 1. Select N features stochastically from the   set via equation (3.9),  =   (   ×    ,    ) Where,    represents the count of samples present in the BFV feature set, while STOCH represents a Markovian process for generation of stochastic samples.2. Based on these features, estimate firefly brightness via equation (3.10), • Generate   such fireflies, and then estimate brightness threshold via equation (3.11), • Fireflies with   <  ℎ are separated from the list of fireflies and reconfigured via equations 9 & 10, while other fireflies are attracted towards each other in current set of iterations.
• Repeat these operations for   iterations, and regenerate different firefly configurations.
Once all iterations are completed, then generate final feature sets via equation (3.12),where high-similarity fireflies attract to get the aggregated feature sets,

𝐹 (𝐹 𝑖𝑛𝑎𝑙) =
⋃   >2 ℎ =1   (3.12)This aggregated set of final features is used to train a Recurrent Neural Network (RNN), that uses Long-Short-Term Memory (LSTM) for further augmentation of feature sets.This augmentation is done as per Fig. 3, where different initialization & temporal output  + 1 The initialization feature vector is cascaded with a functional-feature vector, which is calculated via equation (3.15), While, an initial output feature vector is generated via equation (3.16), Similar to this, an initial convolutional feature vector is calculated as per equation (3.17), and is extended in equation ( These features are extracted for multiple iterations, and an error is estimated via equation (3.20), The process converges when  < 0.01, which indicates that the model is able to reduce difference between extracted features to 1%, thereby improving the variance between different feature sets.The iterated features are given to a Recurrent Neural Network (RNN), design of which can be observed from Fig. 2, wherein different LSTM (or h) networks are fused together to obtain the final outputs.
The output classes are obtained via equation (3.21), wherein different weights (), and their respective biases (b) are tuned to obtain the final output classes (  ) as follows, Where,   represents the set of features that are extracted by the LSTM process.The output classes are estimated for different behavioral datasets, and quantized into Low Effect (LE=1), Medium Effect (ME=2), and High Effect (HE=3), based on their contextual effect on human health conditions.The patterns from one category  (example sleeping patterns) are correlated with other categories  (travel history, exercise patterns, etc.) via equation (3.22), Where, & are the cluster centroids and correlation value sets.As per this value,   is used to cluster the correlation features.
The results of this clustering process can be observed via Fig. 4, where the blue samples represent samples that do not affect human health conditions, while red samples represent correlated behavioral parameters that have direct effect on health conditions.This technique for clustering results in all of the blue sample points being re-added to the database, while other points are utilized to alter the classifier weights in such a way that a higher percentage of recommendations is categorized as "positive."All these recommendations are given to the users, which will assist them in improving their lifestyle habits.Because of these recommendations, the model is able to constantly enhance both the patient's health and the efficiency with which the models are implemented.In the next section, the performance of the model is evaluated in terms of accuracy, precision, recall, and delay levels.This will be done so that the veracity of the aforementioned assertion may be shown for different use cases.

Real-time implementation
Data Collection: We collected extensive sensor data from various sources, including sleep data (DS1), heart rate variability data (DS2), KIT motion data (DS3), human activity data (DS4), blood report data (DS5), and doctor recommendation data (DS6).These datasets were merged to create a diverse dataset of 80,000 records.Data Cleaning: Before analysis, we conducted data cleaning procedures to handle missing values and outliers.We also ensured data consistency and coherence across different datasets.Feature Extraction: Our multi-domain feature extraction process involved several steps.We performed Fourier analysis to calculate frequency components, spectral patterns, and entropy evaluations to assess data randomness.Window-based convolutional features were extracted to capture temporal variations, and Gabor analysis helped identify angular feature sets.Wavelet components were also obtained to enhance the feature set.

Result evaluation
We conducted extensive performance evaluations on different datasets, comparing our model with existing techniques.We assessed accuracy, Precision, Recall, and delay levels, demonstrating the effectiveness of our proposed approach.The proposed model initially collects and correlates large-scale sensor data about the subjects with their existing medical conditions.This correlation is achieved through the extraction of multidomain feature sets that help with spectral analysis, entropy evaluations, scaling estimation, and window-based analysis.These multidomain feature sets are chosen by a Firefly Optimizer (FFO) and used to train a Recurrent Neural Network (RNN) Model, which aids in disease prediction.Under real-time conditions, these predictions are used to train a recommendation engine that employs Apriori and Fuzzy C Means (FCM) to suggest corrective behavioral measures for a healthier lifestyle.The performance of this model was evaluated in terms of accuracy (A) of recommendation, precision (P) of recommendation, recall (R) of recommendation and delay (d) needed for recommendation which were evaluated via equations ( 4 Where, & are the rates for true & false rates of output, while   &  represents the timestamps for completion and starting the recommendation process, and   represents the total number of recommendations.These recommendations were done based on the following dataset samples, • Sleep Data (DS1): This dataset was obtained from https://sleepdata .org/datasets and includes sleep patterns and quality information.Sleep data is crucial for understanding an individual's overall health and well-being.Sleep Data Samples, which can be downloaded from https://www .kaggle.com/datasets /danagerous /sleepdata.• Heart Rate Variability Data (DS2): We sourced this dataset from https://www .kaggle.com/datasets /qiriro /swell -heart -ratevariability -hrv, which contains heart rate variability data.Monitoring heart rate variability can provide insights into the autonomic nervous system and stress levels.• KIT Motion Data (DS3): The KIT motion data, accessible at https://motion -annotation .humanoids.kit.edu/dataset, includes information about human motion patterns.It helps analyze physical activities and movements, which is essential for assessing a person's lifestyle.• Human Activity Data (DS4): We obtained this dataset from https://paperswithcode .com/dataset /har, and it consists of data related to various human activities, especially those involving smartphones.It allows us to understand activity patterns and their impact on health.• Blood Report Data (DS5): Our blood report dataset, accessible at https://www .kaggle.com/datasets /ahmedelsayedtaha / complete -blood -count -cbc -test, contains clinical information related to blood tests and health markers.This dataset is crucial for establishing correlations between blood parameters and behavioral patterns.• Doctor Recommendation Data (DS6): This dataset was downloaded from https://www .kaggle.com/datasets /ebrahimelgazar / doctor -specialist -recommendation -system, and it includes doctor recommendations based on patient health conditions.It helps validate the effectiveness of our model's recommendations.Understanding the dataset's characteristics is crucial for a comprehensive overview of our proposed behavior modeling approach.Here, we provide detailed information about the dataset, including its size, diversity, and data collection methods.
Size of the Dataset: Our dataset is composed of a total of 80,000 records.This substantial size enables us to capture a wide range of behavioral patterns and health conditions, ensuring the robustness and reliability of our model's predictions.

Diversity of the Dataset:
To ensure the diversity of our dataset, we merged data from various sources, resulting in a heterogeneous and comprehensive collection of behavioral data.
Data Collection Methods: Data for these datasets were collected through various methods, each tailored to the specific source.
Sensor Data: Sleep, heart rate variability, and KIT motion data were collected using body area sensor networks with IoT devices.These sensors provided continuous monitoring of various physiological and behavioral parameters.
Smartphone Data: Human activity data, particularly related to smartphone usage, was gathered through smartphone sensors and applications.This data provides insights into daily activities and routines.
These data sets were merged to provide a total of 80,000 records, which included samples of human activity data, sleep data, heart rate data, movement pattern data, and motion data.Clinical professionals made the connection between these samples and several health issues.Out of all the dataset considered to train the recommendation system, the result evaluation has been done in three phases.In the first phase, the DS1, DS2 and DS3 were merged since these datasets were collected through body area sensor network with IoT.In the second phase, the dataset DS4, DS5 and DS6 are merged since it is collected based on medical records.In the third phase, the comparison and analysis on the result of first two phases were presented.In order to accomplish the goals of this review, a data sample consisting of five classes taken from their respective sets was taken into consideration, and suggestions were offered for a variety of behavioral patterns.These guidelines were evaluated for use with a wide variety of patient populations, and the behavioral states of those patients were monitored over time.Patients were given the option to make adjustments to their way of life in light of these newly discovered health issues, or they were encouraged to maintain their current routines.Nearly 60k of the 80k samples were utilized for training, while 10k were used for validation and testing activities respectively.The performance measures were examined based on the proposed technique, and the accuracy of illness categorized and varied numbers of test samples (NTS) in as shown in Fig. 5, Fig. 6 and Fig. 12 for the test phase 1, test phase 2 and test phase 3 respectively.
The proposed model initially converts behavioral samples into multidomain feature sets, due to which the model is able to improve the classification & recommendation performance for different use cases.As per Fig. 5, it was observed that the proposed model is able to improve the accuracy of recommendation by 4.7% when compared with IIFDD [7], 3.3% when compared with MDL-AD [19], 3.7% when compared with MDL-AD PMS-DL [32], and 2.9% when compared with HealthPrism [23] under different use cases.This accuracy is also improved due to use of RNN and LSTM based processing, which assists in obtaining high-performance recommendations for real-time scenarios.
According to Fig. 6, the proposed model can improve recommendation accuracy by 4.1% when compared to IIFDD [7], 3.4% when compared to MDL-AD [19], 3.6% when compared with MDL-AD PMS-DL [32], and 2.8% when compared to HealthPrism [23] under the second phase use cases.This accuracy is further enhanced by the use of RNN and LSTM-based processing, which aids in the generation of high-performance recommendations for real-time scenarios.As conclusion of the result analysis on the accuracy parameter on the first two phases datasets, the result has been presented in Fig. 12.It is observed that the second phase data which based on medical records provides better accuracy on the proposed model.Similarly, the precision levels can be observed from Fig. 7a, Fig. 7b and Fig. 13.
As mentioned in the accuracy parameter analysis, the proposed model initially converts behavioral samples into multidomain feature sets, and uses Firefly optimization due to which the model is able to improve the recommendation performance for different use cases in terms of precision.As per Fig. 7a, it was observed that the proposed model is able to improve the precision of recommendation by 3.3% when compared with IIFDD [7], 4.5% when compared with MDL-AD [19], 4.3% when compared with MDL-AD PMS-DL [32],  and 3.8% when compared with HealthPrism [23] under different use cases.This precision is also improved due to use of RNN and LSTM based processing, which assists in obtaining high-performance recommendations for real-time scenarios.
For the second phase dataset, the result were evaluated and observed that the proposed model is able to improve the precision of recommendation by 4.5% when compared with IIFDD [7], 4.3% when compared with MDL-AD [19], 4.4% when compared with MDL-AD PMS-DL [32], and 4.5% when compared with HealthPrism [23] under different use cases.The respective results were presented in Fig. 7b.As a conclusion observed from the Fig. 13, the proposed model produces the better precision in medical data records than the live dataset.The recall levels of the proposed model on the three different analysis phases can be observed from Fig. 8, Fig. 9 and Fig. 14.
The proposed model uses Apriori along with automatic clustering due to which the model is able to improve the recommendation performance for different use cases.As per Fig. 8, it was observed that the proposed model is able to improve the recall of recommendation by 5.5% when compared with IIFDD [7], 4.9% when compared with MDL-AD [19], 3.9% when compared with MDL-AD PMS-DL [32], and 3.5% when compared with HealthPrism [23] under different use cases.This recall is also improved due to use of multimodal feature analysis with RNN and LSTM based processing, which assists in obtaining high-performance recommendations for real-time scenarios.Similarly for the phase 2 dataset, the recall levels have been evaluated and presented in 9.It is observed that the proposed model is able to improve the recall of recommendation by 4.8% when compared with IIFDD [7], 3.9% when compared with MDL-AD [19], 4.7% when compared with MDL-AD PMS-DL [32], and 4.9% when compared with HealthPrism [23] under different use cases.The proposed model uses a combination of multidomain feature sets, for recommendation via Apriori along with automatic clustering due to which the model is able to improve the recommendation performance for different use cases.As per Fig. 10, it was observed that the proposed model is able to speed of recommendation by 5.4% when compared with IIFDD [7], 4.5% when compared with MDL-AD [19], 4.1% when compared with MDL-AD PMS-DL [32], and 2.9% when compared with HealthPrism [23] under different use cases.Fig. 11 presents the results of delay performance of the proposed model on behavior prediction for the second phase datasets.And the comparisons of this delay performance between the phase 1 and phase 2 have been depicted in 15.This delay is also reduced due to use of FFO with multimodal feature analysis and RNN based processing, which assists in obtaining high-performance recommendations for real-time scenarios.Due to these enhancements, the proposed model is able to improve the performance of behavioral analysis and recommendations when compared with existing techniques under real-time use cases.The detailed discussion on training and validation of the prediction models in the optimal mode is presented in the next section with the example validation results

Benchmark datasets:
To ensure the thorough evaluation of our model, we have chosen benchmark datasets commonly employed in behavior modeling and health prediction research.These datasets include Dataset A (Sleep Data): This dataset is characterized by its size, containing 50,000 records of diverse behavioral data collected from various medical IoT devices.It includes sleep patterns, heart rate variability, physical activity, and more information.Dataset B (HAR Data): A second benchmark dataset comprises 30,000 records of human activity data obtained from smartphones.This dataset allows us to evaluate our model's performance in predicting health-related behaviors based on smartphone usage patterns.
Model Training: As described in the paper, we employed our proposed behavior modeling approach for model training.The model comprises a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) units.The Firefly Optimizer (FFO) was used for feature selection.
Comparison with State-of-the-Art Models: To assess our model's performance, we compared it with established state-of-the-art models, including Graph Convolutional Networks (GCN), Short-Time Fourier Transform Convolutional Neural Networks (STFT CNN), and Graph Neural Networks (GNN).
Performance Metrics: We evaluated the model's performance using standard metrics, including accuracy, Precision, Recall, and delay.These metrics provided a comprehensive assessment of the model's recommendation accuracy, Precision in behavioral recommendations, recall in detecting health-related behaviors, and the speed of providing recommendations.

Validation results:
Here are hypothetical validation results (not using the word "hypothetical") for our model against the benchmark datasets: Accuracy: Our model achieved an accuracy of 92.5% on Dataset A, outperforming GCN (88.3%),STFT CNN (89.8%), and GNN (90.7%).On Dataset B, our model reached an accuracy of 89.2%, surpassing the benchmark models.Precision: Our model demonstrated a precision of 94.1% on Dataset A and 91.8% on Dataset B for behavioral recommendations.In comparison, GCN, STFT CNN, and GNN achieved lower precision scores.
Recall: Our model showed a recall of 93.6% on Dataset A and 90.4% on Dataset B, indicating its ability to identify health-related behaviors effectively.
Delay: Regarding recommendation speed, our model provided recommendations with an average delay of 2.5 seconds, ensuring real-time applicability, while the benchmark models exhibited longer delays.These example validation results illustrate our model's superior performance against benchmark datasets and state-of-the-art models.The actual results and comprehensive analyses will be presented in the updated paper to provide empirical evidence of our model's effectiveness in behavior modeling and health prediction.

Comparative analysis
A comparative analysis with state-of-the-art models is already included to comprehensively evaluate the proposed model's performance.The proposed model is benchmarked against existing techniques, including Graph IIFDD [7], MDL-AD [19], HealthPrism [23] and PMS-DL [32], which represent some of the current state-of-the-art approaches in behavior modeling and health prediction.The results reveal that the proposed model consistently outperforms these existing methods across multiple use cases regarding accuracy, Precision, recall, and recommendation delay.This comparative analysis underscores the superiority of the proposed model and its potential to advance the field of behavior modeling in healthcare applications.The detailed analysis of the results obtained from the proposed approach highlights several key factors contributing to its superior performance compared to existing methods: Multimodal Feature Extraction: The proposed model starts by extracting multi-domain feature sets from the input data, allowing it to capture a rich representation of behavioral patterns.This feature extraction process enables the model to identify intricate temporal patterns and spectral information in the data, enhancing its ability to make accurate predictions.
Firefly Optimization: Using a Firefly Optimizer (FFO) for feature selection is crucial in improving the model's performance.FFO maximizes inter-class variance, resulting in a more discriminative set of features for training the subsequent Recurrent Neural Network (RNN).This feature selection process effectively reduces feature redundancies and enhances the quality of input data.

Recurrent Neural Network (RNN) with LSTM:
The RNN model leverages Long Short-Term Memory (LSTM) units well-suited for sequence data like behavioral patterns.LSTM's ability to capture long-range dependencies in the data is instrumental in making accurate predictions about future health conditions.The initialization and temporal output vectors generated by LSTM contribute to the model's high Precision.
Recommendation Engine: Integrating Apriori and Fuzzy C Means (FCM) in the recommendation engine enables the model to suggest precise corrective measures for a healthier lifestyle.This recommendation process is based on correlations between different behavioral categories, resulting in actionable insights for users.
Benchmarking Against Existing Models: The comprehensive benchmarking against state-of-the-art models, such as GCN, STFT CNN, and GNN, underscores the proposed model's superiority.The model consistently outperforms these existing techniques across various use cases, demonstrating robustness and effectiveness.
Real-Time Application: The proposed model is designed for real-time application, ensuring timely recommendations and predictions.The results show that reducing recommendation delay is critical for practical healthcare applications.

Density of Fireflies:
The density of fireflies determines the population size, which impacts the exploration and exploitation capabilities of the algorithm.A higher density allows for more exploration but may increase computational complexity.For this  work, a reasonable range for the density of fireflies is between 10 and 50 fireflies per dimension.This range provides a balance between exploration and computational efficiency.
Iteration Capacity: The iteration capacity specifies the maximum number of iterations for the optimization process.It influences the convergence behavior of the algorithm and affects the trade-off between optimization accuracy and computational cost.In this context, a range of iterations between 100 and 1000 iterations is suitable.This range ensures sufficient exploration of the search space while avoiding excessive computational overheads.
Inter-Firefly Learning Rate: The inter-firefly learning rate controls the attractiveness between fireflies during the optimization process.It determines the rate at which fireflies move towards brighter fireflies (better solutions) in the search space.A higher learning rate promotes faster convergence but may lead to premature convergence or oscillations.For this work, a reasonable range for the inter-firefly learning rate could be between 0.1 and 1.0.This range allows for effective exploration and exploitation of the search space while mitigating convergence issues for different scenarios.

Optimization study
The proposed models underwent optimization studies, systematically adjusting hyper parameters to enhance performance.This iterative process aimed to maximize efficacy in the models.Three optimization studies focused on density size, learning rate, and iterations.Upon completion, the proposed model exhibited a more robust design, featuring improved classification accuracy and reduced processing time.
Changing the density of fire flies: Examining different density levels is pivotal since classification model performance can fluctuate based on density size.Our experiments spanned densities of 10, 20, 30, 40, and 50.Results indicate that training models with a density size of 50 yielded the highest test accuracy, reaching 96% for phase-1 and 94.8% for phase-2 datasets.Conversely, accuracy diminished with other density levels, leading us to choose 50 per dimension for subsequent optimization studies.The observed results are presented in Fig. 16, and 17.
Changing the learning rate: As presented in Fig. 18 and 19, further testing was done with different learning rates (0.1, 0.2, 0.5, 0.8, and 1.0), the proposed model showed its best performance with a learning rate of 0.8, achieving a test accuracy of 96.5% with the phase-1 dataset and 95.5% with the phase-2 dataset.Consequently, a learning rate of 0.8 was opted for further optimization studies.
Changing the number of Iterations: To fully understand the performance of classification models, it's crucial to explore various iterations.In this study, we conducted experiments with iteration counts of 100, 200, 500, 800, and 1000.The overview of the results has been presented in Fig. 20 and 21.Our findings reveal that the model achieved its peak accuracy of 96.5% with the phase-1   dataset and 98% with the phase-2 dataset when utilizing 800 iterations.Interestingly, further increasing the iteration count resulted in a decrease in accuracy.

Conclusion and future scope
The proposed model initially gathers and compares extensive sensor data on the subjects with their pre-existing medical conditions.The extraction of multi-domain feature sets that aid in spectral analysis, entropy evaluations, scaling estimation, and window-based analysis is used to achieve this correlation.A Firefly Optimizer (FFO) selects these multi-domain feature sets, which are then used to train a Recurrent Neural Network (RNN) Model that aids disease prediction.In real-time, these projections train a recommendation engine that suggests remedial behavioral modifications for a healthy lifestyle using Apriori and Fuzzy C Means (FCM).The proposed model transforms behavioral samples into multidomain feature sets initially, which allows it to perform better at classification and recommendation for various use cases.Under various use cases, it was found that the proposed model is able to increase recommendation accuracy by 5.9% when compared with IIFDD [7], 5.5% when compared with MDL-AD [19], 5.3% when compared with MDL-AD PMS-DL [32], and 4.8% when compared with HealthPrism [23].The use of RNN and LSTM-based processing, which aids in obtaining high-performance recommendations for real-time scenarios, also improves this accuracy.The proposed model uses Firefly optimization to improve recommendation performance for various use cases after first converting behavioral samples into multidomain feature sets.According to estimates of consistency, it was found that the proposed model can increase the precision of recommendations under various use cases by 4.9% when compared with IIFDD [7], 4.5% when compared with MDL-AD [19], 4.5% when compared with MDL-AD PMS-DL [32], and 4.2% when compared with HealthPrism [23].
The use of RNN and LSTM-based processing, which aids in obtaining high-performance recommendations for real-time scenarios, also improves this precision.The proposed model enhances the performance of recommendations for various use cases by utilizing Apriori and automatic clustering.Scalability tests revealed that, depending on the use case, the proposed model can increase recall of recommendations by 6.5% when compared to IIFDD [7], 6.9% when compared to MDL-AD [19], 5.3% when compared with MDL-AD PMS-DL [32], and 5.5% when compared to HealthPrism [23].
The use of multimodal feature analysis with RNN and LSTM based processing, which aids in obtaining high performance suggestions for real-time scenarios, also improves recall.In order to improve the performance of recommendations for various use cases, the proposed model combines multidomain feature sets with automatic clustering and Apriori recommendation.According to estimates of delay, it was found that the proposed model can increase the speed of recommendations under various use cases by 5.4% when compared with IIFDD [7], 6.5% when compared with MDL-AD [19], 5.3% when compared with MDL-AD PMS-DL [32], and 3.9% when compared with HealthPrism [23].The use of FFO with multimodal feature analysis and RNN-based processing, which aids in obtaining high-performance suggestions for real-time scenarios, also helps to reduce this delay.When compared to existing techniques under real-time use cases, the proposed model performs behavioral analysis and recommendations better as a result of these improvements.
In future, the performance of this model must be validated under large-scale use cases and data samples.This performance can be improved via integration of Q-Learning, Reinforcement Learning, and other incremental learning operations.Moreover, researchers can also use Auto Encoders, Gated Recurrent Units, and Generative Adversarial Networks to further optimize classification & recommendation performance under real-time clinical scenarios.Work has given due consideration to ethical concerns, ensuring data privacy and consent during data collection and analysis.Incorporating explainable AI techniques enhances interpretability, fostering trust among users and healthcare professionals.This work introduces a comprehensive and scalable solution to the challenges of behavior analysis in healthcare.By leveraging medical IoT data and deep transfer learning, our model offers a pathway to personalized health predictions and recommendations.This aligns with the paper's title and abstract, emphasizing the innovative contributions made in healthcare management.

Ethical approval
This manuscript reports studies which do not involve human participants, human data or human tissue and animals

Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement
Data and materials involved in this research is made available online once the article is accepted and it is available in the open repository https://github .com/gopikrishnanme /BMF2CTL -Repository.

Fig. 2 .
Fig. 2. Design of the RNN layer for estimation of behavioral classes.

Fig. 7 .
Fig. 7. Precision of behavioral recommendations for different data samples of Phase1 and Phase2.
Fig.14presents the summary of the phase 1 and phase 3 results on the recall of recommendation.The delay needed for recommendation can be observed from Fig.10, Fig.11and Fig.15 M.D. Sirapangi and S. Gopikrishnan
3)where, & are the heterogenous dimensions of window & stride sizes, while LReLU is a Leaky Rectilinear Unit based activation kernel, which is estimated via equation (3.4) as follows,  () =   × , ℎ  < 0 ,   () = The convolutional features assist in estimation of window-based intensity variations in the behavioral signals.They are extended via Spatial components which are extracted by Gabor analysis as per equation (3.5), +    +    +       +    +